BİTCOİN İŞLEMLERİNDE HİLELİ FAALİYET ÖRÜNTÜLERİNİN TESPİTİ: ŞÜPHELİ CÜZDAN DAVRANIŞLARININ ANALİZİ

Abstract

Bu çalışma, cüzdan düzeyindeki davranışların analizi yoluyla Bitcoin işlemlerindeki potansiyel dolandırıcılık faaliyetlerini belirlemek için örüntü tabanlı bir yaklaşım geliştirmekte ve uygulamaktadır. Toplam 8.526 Bitcoin cüzdanından oluşan bir veri kümesini inceleyerek, beş şüpheli işlem modelinden en az birini sergileyen 72 cüzdan (%0,84) tespit ettik: tek seferlik yüksek değerli transferler, potansiyel karıştırma hizmetleri, önemli cüzdanların aniden boşaltılması, anormal işlem oranları ve büyük hareketsiz cüzdanlar. Sayıları az olmasına rağmen, bu şüpheli cüzdanlar 777,15 BTC'yi kontrol ediyordu ve bu da veri kümesindeki toplam Bitcoin'in %9,39'unu temsil ediyordu. İstatistiksel analiz, şüpheli ve şüpheli olmayan cüzdanlar arasında önemli farklılıklar olduğunu ortaya koymuştur; şüpheli cüzdanlar 11,9 kat daha yüksek ortalama işlem değerleri, 12,3 kat daha yüksek ortalama bakiyeler ve önemli ölçüde daha yüksek işlem sıklığı göstermektedir. Çapraz desen analizi, şüpheli cüzdanların %26,4'ünün aynı anda birden fazla şüpheli desen sergilediğini ortaya koyarak koordineli suç stratejilerine işaret etmiştir. Belirlenen kalıplar, kara para aklama, fidye yazılımı ödeme işlemleri ve yasadışı fon depolama gibi bilinen kripto para birimi destekli suçlarla uyumludur. Bu araştırma, şüpheli işlem modellerinin bir tipolojisini oluşturarak, bunların finansal etkilerini ölçerek ve kripto para ağlarında potansiyel olarak hileli faaliyetlerin tespitini iyileştirebilecek gelişmiş izleme sistemleri için bir çerçeve sağlayarak kripto para güvenliğine katkıda bulunmaktadır.

Keywords

• Bitcoin dolandırıcılığı tespiti
• kripto para birimi güvenliği
• şüpheli işlem kalıpları
• kara para aklama
• blok zinciri adli tıp

DETECTING FRAUDULENT ACTIVITY PATTERNS IN BITCOIN TRANSACTIONS: AN ANALYSIS OF SUSPICIOUS WALLET BEHAVIORS

Abstract

This study develops and applies a pattern-based approach to identify potentially fraudulent activity in Bitcoin transactions through the analysis of wallet-level behaviors. Examining a dataset of 8,526 Bitcoin wallets, we identified 72 wallets (0.84%) exhibiting at least one of five suspicious transaction patterns: one-time high-value transfers, potential mixing services, sudden draining of significant wallets, abnormal transaction rates, and large dormant wallets. Despite their small number, these suspicious wallets controlled 777.15 BTC, representing 9.39% of the total Bitcoin in the dataset. Statistical analysis revealed significant differences between suspicious and non-suspicious wallets, with suspicious wallets showing 11.9 times higher average transaction values, 12.3 times higher average balances, and substantially greater transaction frequencies. Cross-pattern analysis found that 26.4% of suspicious wallets exhibited multiple suspicious patterns simultaneously, suggesting coordinated criminal strategies. The identified patterns align with known cryptocurrency-facilitated crimes such as money laundering, ransomware payment processing, and illicit fund storage. This research contributes to cryptocurrency security by establishing a typology of suspicious transaction patterns, quantifying their financial impact, and providing a framework for enhanced monitoring systems that could improve detection of potentially fraudulent activity across cryptocurrency networks.

Keywords

• Bitcoin fraud detection
• cryptocurrency security
• suspicious transaction patterns
• money laundering
• blockchain forensic

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